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Models have evolved from mere pictures supporting human understanding and communication to sophisticated knowledge structures processable by machines and establish value through their processing capabilities. This entails an inevitable need for computer-understandable modeling languages and causes formalization to be a crucial part in the lifecycle of engineering a modeling method. An appropriate formalism must be a means for providing a structural definition to enable a theoretical investigation of conceptual modeling languages and a unique, unambiguous way of specifying the syntax and semantics of an arbitrary modeling language. For this purpose, it must be generic and open to capturing any domain and any functionality. This paper provides a pervasive description of the formalism MetaMorph based on logic and model theory—an approach fulfilling the requirements above for modeling method engineering. The evaluation of the formalism is presented following three streams of work: First, two evaluative case studies illustrate the applicability of MetaMorph formalism concept by concept on the modeling language ProVis from the domain of stochastic education and the well-known Entity-Relationship language. ProVis as well as ER comprise only a few objects and relation types but with high interconnection and expressive power and are therefore considered interesting specimens for formalization. Second, a comprehensive juxtaposition of MetaMorph to three other formalization approaches based on different foundational theories is outlined concept by concept to underpin the formalism design. Third, an empirical evaluation has been performed, assessing the usability and adequacy of the formalism within a classroom assessment. The results allow for conclusions on the completeness, intuitiveness, and complexity as well as on interdependencies with engineers’ skills.

Zusammenfassung Vermehrt werden Daten in der Produktion erfasst und mit Hilfe maschineller Lernverfahren ausgewertet. Ziel dieser Datenanalysen ist es, Informationen über die Produktion zu erhalten. Typische Anwendungsbeispiele sind die Vorhersage der Produktqualität sowie die vorausschauende Wartung und Instandhaltung. Allerdings führen Datenanalysen aufgrund der domänenspezifischen Herausforderungen häufig zu keinen ausreichenden Analyseergebnissen. Die Kombination von Datenanalysen mit Expertenwissen stellt hierbei einen vielversprechenden Ansatz zur Lösung der domänenspezifischen Problemstellungen dar. Dieser Beitrag stellt hierzu eine neue Vorgehensweise für die Durchführung von Projekten des maschinellen Lernens mit wissensbasierten Ansätzen in der Produktion vor. In einem Ablaufplan werden notwendige Schritte und die Beteiligung der entsprechenden Experten vorgestellt. Für die Nutzung von vorhandenem Domänenwissen in der Produktion werden in diesem Artikel verschiedene Methoden zur Wissensmodellierung aufgezeigt und diskutiert. Der Ablaufplan und die Methoden zur Wissensmodellierung werden anschließend am Beispiel einer servopneumatischen Schweißzange validiert.

Context. Mindfulness is a meditation technique whose main goal is keeping the mind calm and educating attention by focusing only on one thing at a time, usually breathing. The reported benefits of its continued practice can be of interest for Software Engineering students and practitioners, especially in tasks like conceptual modeling, in which concentration and clearness of mind are crucial. Goal. In order to evaluate whether Software Engineering students enhance their conceptual modeling performance after several weeks of mindfulness practice, a series of three controlled experiments were carried out at the University of Seville during three consecutive academic years (2013–2016) involving 130 students. Method. In all the experiments, the subjects were divided into two groups. While the experimental group practiced mindfulness, the control group was trained in public speaking as a placebo treatment. All the subjects developed two conceptual models based on a transcript of an interview, one before and another one after the treatment. The results were compared in terms of conceptual modeling quality (measured as effectiveness, i.e. the percentage of model elements correctly identified) and productivity (measured as efficiency, i.e. the number of model elements correctly identified per unit of time). Results. The statistically significant results of the series of experiments revealed that the subjects who practiced mindfulness developed slightly better conceptual models (their quality was 8.16% higher) and they did it faster (they were 46.67% more productive) than the control group, even if they did not have a previous interest in meditation. Conclusions. The practice of mindfulness improves the performance of Software Engineering students in conceptual modeling, especially their productivity. Nevertheless, more experimentation is needed in order to confirm the outcomes in other Software Engineering tasks and populations. Ministerio de Economía y Competitividad TIN2015–70560–R Junta de Andalucía US–1264651 Ministerio de Ciencia, Innovación y Universidades RTI2018–101204–B–C22 Ministerio de Ciencia, Innovación y Universidades RTI2018- 101204–B–C21 Junta de Andalucía P18–FR–2895

Background Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. Results In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the “ontological unpacking” method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. Conclusions We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the “I” in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

Scientific output of the project "Improving Resilience for the Rio Grande Coupled Human-Natural System," supported by Grant No. G15AP00132 from the United States Geological Survey.

[EN] Precision medicine has emerged as a disrupting medical model to transform a historically reactive medicine into a proactive one that focuses on delivering individualized treatment. A relevant challenge of precision medicine is to integrate the large amount of omics data that exists. This data has a high degree of heterogeneity, dispersion, and isolation. In addition, there is a lack of a solid ontological commitment regarding domain concepts and definitions, and a unified guideline of how to transform data into knowledge is missing. In this work, we report our experience applying conceptual modeling to deal with these challenges in a specific genomics dimension, i.e., Precision Medicine. To do so, we have applied conceptual modeling techniques. The use of these techniques allows us to create representations of the world (i.e., conceptual schemes) that can be used for the purposes of understanding, communicating, and problem-solving. They also help to establish a common ontological framework to facilitate both communication and knowledge evolution in complex domains. We identify a set of limitations that emerged after working in a precision medicine context, and we describe how we have solved them using conceptual modeling. Thus, the main contribution of this work is to present the subsequent Conceptual Schema that allowed us to overcome these limitations, which provides a better representation of proteomics data and eases its integration., This work was supported in part by the Spanish State Research Agency and the Generalitat Valenciana under Project PROMETEO/2018/176, Project PDC2021-121243-I00, and Project INNEST/2021/57; and in part by the European Regional Development Fund (ERDF) and the European Union NextGenerationEU/PRTR.

In computer science, models are made explicit to provide formality and a precise understanding of small, contingent universes (e.g., an organization), as constructed from stakeholder requirements. Conceptual modeling is a fundamental discipline in this context whose main concerns are identifying, analyzing and describing the critical concepts of a universe of discourse. In the information systems field, one of the reasons why projects fail is an inability to capture requirements in a way that can be technically used to configure a system. This problem of requirements specification is considered to have deficiencies in theory. We apply a recently developed model called the Thinging Machine (TM) model which uniformly integrates static and dynamic modeling features to this problem of requirements specification. The object-Oriented (OO) approach to modeling, as applied in Unified Modeling Language, is by far the most applied and accepted standard in software engineering; nevertheless, new notions in the field may enhance and facilitate a supplementary understanding of the OO model itself. We aim to contribute to the field of conceptual modeling by introducing the TM model s philosophical foundation of requirements analysis. The TM model has only five generic processes of things (e.g., objects), in which genericity indicates generality, as in the generic Aristotelian concepts based on abstraction. We show the TM model s viability by applying it to a real business system., 15 pages, 16 figures

Data reuse and meta-data handling remain a tricky problem for both designers and managers, especially when schemas reverse engineering is on demand and some data are stored in materialized views. In this paper, we tackle this problem by using ontology-based meta-materialized views. Indeed, ontologies, which are semantics-based, ensure the stability of the underlying schemas of the data repositories and the ease of the overall access and processing of the data and meta-data. Our proposal is informed by a set of conceptual guidelines and outlined through a case study example. ACM Computing Classification System (1998): H.2.1, H.2.2, H.2.3, H.2.8, K.6.1. Mathematics Subject Classification – MSC2020: 62D10, 62H20.

The design of complex man-made systems mostly involves a conceptual modeling phase; therefore, it is important to ensure an appropriate analysis method for these models. A key concept for such analysis is the development of a diagramming technique (e.g., UML) because diagrams can describe entities and processes and emphasize important aspects of the systems being described. The analysis also includes an examination of ontological concepts such as states and events, which are used as a basis for the modeling process. Studying fundamental concepts allows us to understand more deeply the relationship between these concepts and modeling frameworks. In this paper, we critically analyze the classic definition of a state utilizing the thinging machine (TM) model. States in state machine diagrams are considered the appropriate basis for modeling system behavioral aspects. Despite its wide application in hardware design, the integration of a state machine model into a software system s modeling requirements increased the difficulty of graphical representation (e.g., integration between structural and behavioral diagrams). To understand such a problem, in this paper, we project (create an equivalent representation of) states in TM machines. As a case study, we re-modeled a state machine of an assembly line system in a TM. The outcome is a complicated picture of assembly line behavior. Therefore, as an alternative solution, we re-modeled the assembly line based solely on the TM. This new model presents a clear contrast between state-based modeling of assembly line behavior and the TM approach. The TM modeling seems more systematic than its counterpart, the state machine, and its notions are well defined. In a TM, states are just compound events. A model of a more complex system than the one in the assembly line has strengthened such a conclusion., Comment: 12 pages, 20 figures

In previous works, we proposed a one-category (entitled thimac) conceptual model called a thinging machine (TM), which integrates staticity (e.g., objects) and dynamism (e.g., events) without losing valuable aspects of diagrammatic intuition in conceptual modeling. We proposed applying TM to conceptual modeling in software engineering (e.g., on or above the level of UML as a conceptual modeling language). In this paper, to show such an application in software engineering, we first present a complete high-level description of a library service system to demonstrate the TM s applicability. Furthermore, we explore the TM s features, emphasizing the realization of thimacs as events. The purpose is to develop better understanding of the TM notions by contrasting them with their uses in related fields. The notion of an event plays a prominent role in many fields of study, including philosophy, linguistics, literary theory, probability theory, artificial intelligence, physics, and history. A TM event is a static thimac with a time breath (time subthimac) that infuses dynamism into the thimac. It arises from how the TM static region is infected with time. Such a view is contrasted with some philosophical and linguistics definitions of an event (e.g., unit of experience of Whitehead). We also raise interesting issues (e.g., event movement) in this study., Comment: 12 pages, 21 figures

International audience; For some time, and even more so now, Fake News has increasingly occupied the media and social space. How identify Fake News and conspiracy theories have become an extremely attractive research area. However, the lack of a solid and well-founded conceptual characterization of what exactly Fake News is and what are its main characteristics, makes it difficult to manage their understanding, identification, and detection. This research work advocates that conceptual modeling must plays a crucial role in characterizing Fake News content accurately. Only by delimiting what Fake News is will it be possible to understand and manage their different perspectives and dimensions, with the ultimate goal of developing a reliable framework for online Fake News detection, as much automated as possible. To contribute in that direction from a pure and practical conceptual modeling perspective, this paper proposes a precise conceptual model of Fake Newss, an essential element for any explainable Artificial Intelligence (XAI)-based approach that must be based on the shared understanding of the domain that only such an accurate conceptualization dimension can facilitate.

[Context and Motivation] User stories are a popular notation for representing requirements, especially in agile development. Although they represent a cornerstone for developing systems, limited research exists on how user stories are refined into conceptual design. [Question/Problem] We study the process of deriving conceptual models from user stories, which is at the basis of information systems development. We focus our attention on the derivation of a holistic view of structural and interaction aspects, represented via class diagrams and use case diagrams, respectively. In this paper, we examine whether providing guidelines has an effect on the ability of humans to derive complete and valid conceptual models. [Principal Ideas/Results] We design example-based guidelines for the derivation of class and use case diagrams from user stories. Then, we conduct a two-factor, two-treatment controlled experiment with 77 undergraduate students serving as subjects. The results indicate that the guidelines improve the completeness and validity of the conceptual models in cases of medium complexity, although the subjects were neutral on the usefulness of the guidelines. [Contribution] The guidelines may assist analysts in the refinement of user stories. Our initial evidence, besides showing how the guidelines can help junior analysts derive high-quality conceptual models, opens the doors for further studies on the refinement of user stories, and to the investigation of alternative guidelines.

Multi-level modeling (MLM) as part of object-oriented modeling aims at fully utilizing the expressive power of multiple abstraction levels. While these levels where initially used to define domain-specific modeling languages, i.e. for linguistic purposes, the MLM community has long argued that there is much more to gain by tapping into ontological abstraction levels. While MLM is a rather specialized research field, there are now quite a number of different proposals. There is thus an opportunity to develop a uniform core of MLM that then possibly can become part of a standard and be taken up by the larger modeling community.

We present a framework to support creative design of emergency management scenarios. By creative design of scenarios we mean the process of imagining situations and describing them through models and stories. The framework supports the tasks of gathering and organizing knowledge about emergency management situations by automatically generating conceptual models, related to fragments of emergency scenarios. It leverages semantics-based techniques to enable a computational creativity approach. A software application was defined to support the activities of modeling scenarios by permitting to generate, organize, and query sets of these conceptual models, which we name mini-stories, and that can be adopted to inspire the activity of creative design. Selected mini-stories are blueprints for more detailed user scenario descriptions and models that can be used, for instance, for analysis or simulation. As a case study, we consider emergency management in smart cities. This is a challenging domain because smart cities are characterized by interconnected physical and virtual services forming complex ecosystems, which provide sophisticated services to the population and to institutions, manage public resources in a optimal way, and involve citizens in decisional processes. As a consequence, smart city ecosystems can be threatened by several hazards spanning from natural disasters, as tsunami and earthquakes, to anthropic events, as terrorist attacks. Ability of service providers and institutional operators to face and manage emergency situations is therefore a relevant issue. Simulation and analysis of both crisis events and executions of management plans are a promising approach to deal with these articulated problems. However, manual definition of models to base the analysis is a demanding activity due to the huge number of different situations to consider. It requires knowledge related to the crisis and emergency domains, to the context (e.g., a specific city and its current regulations) and ability in modeling tasks. All these aspects demand for tools to support modeling activities, and our proposal aims at fulfilling this need. In particular, the discussed framework uses in a integrated way three types of knowledge: structural knowledge, to support the construction of models based on design patterns; domain knowledge, here related to smart cities and emergency management and represented by means of ontologies; and contextual knowledge, related to specific aspects (e.g., localization) of the considered scenario and represented as rules. We validated the presented approach by means of experiments performed by real city planners. © 2018 Elsevier Ltd

Conceptual modeling plays a fundamental role in information systems engineering, and in data and systems interoperability. To play their role as instruments for domain modeling, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, conceptual modelers are subject to cognitive limitations and biases and, hence, in practice, they systematically produce models that fall short in that respect. Moreover, automating the process of formally assessing conceptual models in this sense (i.e., model validation) is notoriously hard, mainly because the intended worldview at hand lies in the mind of these stakeholders. In this paper, we provide a novel approach to model validation and automated constraint learning that combines, on one hand, Model Finding via the visual simulation of that model’s valid instances and, on the other hand, Inductive Logic Programming techniques. In our approach, we properly channel the results produced by the application of a visual model finding technique as input to a learning process. We then show how the approach is able to support the modeler in identifying missing constraints from the original model. The approach is validated against a catalog of empirically-elicited conceptual modeling anti-patterns. As we show here, the approach is able to support the automated learning of constraints that are needed to rectify a number of relevant anti-patterns in this catalog.

Classical data models such as the entity-relationship diagrams distinguish between objects(=entities) and values. An object is referenced by its immutable identifier that is assigned to the object when it is created. Its state is established by a combination of mutable values taken from so-called domains. Domains are sets of values that come with their own algebraic semantics, such as integer numbers or strings. So, values have no identity and do not change. Objects are identified and their state changes. In this paper, we challenge this strict dichotomy by introducing so-called attribute objects. ©2021 by the author — Open Access — Distributed under CC BY 4.0Academia Letters is a by ACADEMIA.EDU founded in 2020 with peer reviews but strictly limited word-count.

This paper is in the intersection of software engineering and system engineering, two intimately intertwined disciplines. A dominating theme in this paper is the integral conceptualization of systems at large, as well as an underlying concern with software systems. In the software development life cycle, challenges still exist in translating requirements into a design artifact and then into an implementation (e.g., coding), then validating the results. From our perspective, software engineering requires an integrating paradigm toward a unified modeling orientation. Many methodologies, languages, and tools exist for facilitating system development processes. This paper is a venture into project development. To focus the materials, we concentrate on Harel s novel (and classic) development environment, which integrates a scenario-based engineering object orientation and statecharts through developing a railcar system. The railcar system is used as a detailed sample of translating requirements into a design artifact and then into an implementation, then validating the result. The project is re-cased as a single integrated modeling endeavor to be contrasted with the scenario and statecharts development. The result of this scheme is an enriched understanding through experimenting with and contrasting various development methods of software projects., Comment: 11 pages, 17 figures

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We study the usage of negation in JSON Schema data modeling. Negation is a logical operator rarely present in type systems and schema description languages, since it complicates decision problems: many software tools, but also formal frameworks for working with JSON Schema, do not fully support negation. This motivates us to study whether negation is actually used in practice, for which aims, and whether it could—in principle—be replaced by simpler operators. We have collected a large corpus of 80k open source JSON Schema documents from GitHub. We perform a systematic analysis, quantify usage patterns of negation, and also qualitatively analyze schemas. We show that negation is indeed used, albeit infrequently, following a stable set of patterns.

Technological advances in information sharing have raised concerns about data protection. Privacy policies contain privacy-related requirements about how the personal data of individuals will be handled by an organization or a software system (e.g., a web service or an app). In Europe, privacy policies are subject to compliance with the General Data Protection Regulation (GDPR). A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR. Incomplete privacy policies might result in large fines on violating organization as well as incomplete privacy-related software specifications. Manual completeness checking is both time-consuming and error-prone. In this paper, we propose AI-based automation for the completeness checking of privacy policies. Through systematic qualitative methods, we first build two artifacts to characterize the privacy-related provisions of GDPR, namely a conceptual model and a set of completeness criteria. Then, we develop an automated solution on top of these artifacts by leveraging a combination of natural language processing and supervised machine learning. Specifically, we identify the GDPR-relevant information content in privacy policies and subsequently check them against the completeness criteria. To evaluate our approach, we collected 234 real privacy policies from the fund industry. Over a set of 48 unseen privacy policies, our approach detected 300 of the total of 334 violations of some completeness criteria correctly, while producing 23 false positives. The approach thus has a precision of 92.9% and recall of 89.8%. Compared to a baseline that applies keyword search only, our approach results in an improvement of 24.5% in precision and 38% in recall.

Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research. The research reported in this paper was partially funded by the ECLIPSE-UA (RTI2018-094283-B-C32) and the AETHER-UA (PID2020-112540RB-C43) Projects from the Spanish Ministry of Science and Innovation.

The general acceptance of sequence diagrams can be attributed to their relatively intuitive nature and ability to describe partial behaviors (as opposed to such diagrams as state charts). However, studies have shown that over 80 percent of graduating students were unable to create a software design or even a partial design, and many students had no idea how sequence diagrams were constrained by other models. Many students exhibited difficulties in identifying valid interacting objects and constructing messages with appropriate arguments. Additionally, according to authorities, even though many different semantics have been proposed for sequence diagrams (e.g., translations to state machines), there exists no suitable semantic basis refinement of required sequence diagram behavior because direct style semantics do not precisely capture required sequence diagram behaviors; translations to other formalisms disregard essential features of sequence diagrams such as guard conditions and critical regions. This paper proposes an alternative to sequence diagrams, a generalized model that provides further understanding of sequence diagrams to assimilate them into a new modeling language called thinging machine (TM). The sequence diagram is extended horizontally by removing the superficial vertical-only dimensional limitation of expansion to preserve the logical chronology of events. TM diagramming is spread nonlinearly in terms of actions. Events and their chronology are constructed on a second plane of description that is superimposed on the initial static description. The result is a more refined representation that would simplify the modeling process. This is demonstrated through remodeling sequence diagram cases from the literature., 11 pages, 11 figures

Background This 4-year research project focuses on 6 social community enterprises (SCEs) that operate in 5 neighborhoods in a Dutch city. Residents of these neighborhoods face problems such as poor average levels of physical and mental health, high unemployment rates, and weak social cohesion. SCEs offer residents social, cultural, and work-related activities and are therefore believed to help these persons develop themselves and strengthen the social ties in the community. Because of a lack of empirical evidence; however, it is unclear whether and how SCEs benefit the health and well-being of participants. Objective This paper outlines a protocol for an evaluation study on the impact of SCEs, aiming to determine (1) to what extent SCEs affect health and well-being of participating residents, (2) what underlying processes and mechanisms can explain such impact, and (3) what assets are available to SCEs and how they can successfully mobilize these assets. Methods A mixed methods multiple-case study design including repeated measurements will be conducted. Six SCEs form the cases. An integrated model of SCE health intervention will be used as the theoretical basis. First, the impact of SCEs is measured on the individual and community level, using questionnaires and in-depth interviews conducted with participants. Second, the research focuses on the underlying processes and mechanisms and the organizational and sociopolitical factors that influence the success or failure of these enterprises in affecting the health and well-being of residents. At this organizational level, in-depth interviews are completed with SCE initiators and stakeholders, such as municipal district managers. Finally, structurally documented observations are made on the organizational and sociopolitical context of the SCEs. Results This research project received funding from the Netherlands Organization for Health Research and Development in 2018. Data collection takes place from 2018 until 2022. Data analysis starts after the last round of data collection in 2022 and finalizes in 2024. Expected results will be published in 2023 and 2024. Conclusions Despite the societal relevance of SCEs, little empirical research has been performed on their functioning and impact. This research applies a variety of methods and includes the perspectives of multiple stakeholders aiming to generate new empirical evidence. The results will enable us to describe how SCE activities influence intermediate and long-term health outcomes and how the organizational and sociopolitical context of SCEs may shape opportunities or barriers for health promotion. As the number of these initiatives in the Netherlands is increasing rapidly, this research can benefit many SCEs attempting to become more effective and increase their impact. The findings of this research will be shared directly with relevant stakeholders through local and national meetings and annual reports and disseminated among other researchers through scientific publications. International Registered Report Identifier (IRRID) DERR1-10.2196/37966

General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies what exists in reality. A widely used ontology in information systems, especially for conceptual modeling, is the BWW (Bunge–Wand–Weber), which is based on ideas of the philosopher and physicist Mario Bunge, as synthesized by Wand and Weber. The ontology was founded on an early subset of Bunge’s philosophy; however, many of Bunge’s ideas have evolved since then. An important question, therefore, is: do the more recent ideas expressed by Bunge call for a new ontology? In this paper, we conduct an analysis of Bunge’s earlier and more recent works to address this question. We present a new ontology based on Bunge’s later and broader works, which we refer to as Bunge’s Systemist Ontology (BSO). We then compare BSO to the constructs of BWW. The comparison reveals both considerable overlap between BSO and BWW, as well as substantial differences. From this comparison and the initial exposition of BSO, we provide suggestions for further ontology studies and identify research questions that could provide a fruitful agenda for future scholarship in conceptual modeling and other areas of information technology.

Permafrost is a large reservoir of soil organic carbon, accounting for about half of all the terrestrial storage, almost equivalent to twice the atmospheric carbon storage. Hence, permafrost degradation under global warming may induce a release of a substantial amount of additional greenhouse gases, leading to further warming. In addition to gradual degradation through heat conduction, the importance of abrupt thawing or erosion of ice-rich permafrost has recently been recognized. Such ice-rich permafrost has evolved over a long timescale (i.e., tens to hundreds of thousands of years). Although important, knowledge on the distribution of vulnerability to degradation, i.e., location and stored amount of ground ice and soil carbon in ice-rich permafrost, is still limited largely due to the scarcity of accessible in situ data. Improving the future projections for the Arctic using the Earth System Models will lead to a better understanding of the current vulnerability distribution, which is a prerequisite for conducting climatic and biogeochemical assessment that currently constitutes a large source of uncertainty. In this study, present-day circum-Arctic distributions (north of 50° N) in ground ice and organic soil carbon content are produced by a new approach to combine a newly developed conceptual carbon-ice balance model, and a downscaling technique with the topographical and hydrological information derived from a high-resolution digital elevation model (ETOPO1). The model simulated the evolution of ground ice and carbon for the recent 125 thousand years (from the Last Interglacial to the present) at 1° resolution. The 0.2° high-resolution circum-Arctic maps of the present-day ground ice and soil organic carbon, downscaled from the 1° simulations, were reasonable compared to the observation-based previous maps. These data, together with a map of vulnerability of ice-rich permafrost to degradation served as initial and boundary condition data for model improvement and the future projection of additional greenhouse gas release potentially caused by permafrost degradation.

The first meeting of the Special Interest Group on Conceptual Modeling (June 6, 2019) convened an international, interdisciplinary group of scholars that work in the organization and representation of information. Presenters shared emerging issues in the field, and attendees developing an agenda for conceptual modeling research needs into the future. This report summarizes presentations and discussions from the workshop.

In recent years, there has been a dramatic increase in both practical and research applications of unmanned aerial vehicles (UAVs). According to the literature, there is a need in this area to develop a more refined model of UAV system architecture, in other words, a conceptual model that defines the system s structure and behavior. The existing models mostly are fractional and do not account for the entire important dynamic attributes. Progress in this area could reduce ambiguity and increase reliability in the design of such systems. This paper aims to advance the modeling of UAV system architecture by adopting a conceptual model called a thinging (abstract) machine in which all of the UAV s software and hardware components are viewed in terms of the flow of things and five generic operations. We apply this model to a real case study of a drone. The results, an integrated conceptual representation of the drone, support the viability of this approach., Comment: 10 pages, 15 figures

[Context and motivation] Notations for expressing requirements are often proposed without explicit consideration of their suitability for specific tasks. Consequently, practitioners may choose a sub-optimal notation, thereby affecting task performance. [Question/problem] We investigate the adequacy of two well-known notations: use cases and user stories, as a starting point for the manual derivation of a static conceptual model. In particular, we examine the completeness and correctness of the derived conceptual model. [Principal ideas/results] We conducted a two-factor, two-treatment controlled experiment with 118 subjects. The results indicate that for deriving conceptual models, user stories fit better than use cases. It seems that the repetitions in user stories and their conciseness contribute to these results. [Contribution] The paper calls for evaluating requirements notations in the context of various requirements engineering tasks and for providing evidence regarding the aspects that need to be taken into account when selecting a requirement notation.

Part 4: Business Processes; International audience; When it comes to the economic and strategic development of companies, maturity models are regarded as silver bullets. However, the existing discrepancy between the large amount of existing, differently developed models and their rare application remains astonishing. We focus on this phenomenon by analyzing the models’ interpretability and possible structural and conceptual inconsistencies. By analyzing existing, staged maturity models, we develop a meta model for staged maturity models so different maturity models may share common semantics and syntax. Our meta model can therefore contribute to the conceptual rigor of existing and future maturity models in all domains and can be decisive for the success or failure of a maturity measurement in a company.

[EN] Currently, data management in oncology department is complex and requires advanced Information Systems (ISs) to process data where "omic" information should be integrated together with patient's clinical data to improve data analysis and decision-making process. This research paper reports a practical experience in this context. A Conceptual Model (CM) has been designed to develop an Information System (IS) in order to manage clinical, pathological, and molecular data in a holistic way at the oncology department of two main Hospitals in Paraguay. Additionally, model-based archetypes have been proposed to specify the selected user interaction strategy. The CM and its associated archetypes are the basis to develop a clinical IS in order to load -firstly- and manage -secondly- all the clinical data that the domain requires, showing how feasible the approach is in practice, and how much the corresponding clinical data management is improved. In this work, we want to reinforce with this real experience how using a CM along with archetypes correctly helps to design, develop and manage better information systems, emphasizing the relevance of the selected clinical domain. [ES] Actualmente, la gestión de datos en el departamento de oncología es compleja y requiere sistemas de información avanzados para procesar datos donde la información "ómica" debe integrarse junto con los datos clínicos del paciente para mejorar el análisis de datos y el proceso de toma de decisiones. Este trabajo de investigación presenta una experiencia práctica en este contexto. Se ha diseñado un Modelo Conceptual (MC) para desarrollar un Sistema de Información (SI) con el fin de gestionar datos clínicos, patológicos y moleculares de manera integral en el departamento de oncología de dos hospitales principales en Paraguay. Además, se han propuesto arquetipos basados en modelos para especificar la estrategia de interacción del usuario. El MC y los arquetipos asociados son la base para desarrollar un SI clínico con el fin de cargar -primero- y gestionar -segundo- todos los datos clínicos que requiere el dominio, mostrando cuán factible es el enfoque en la práctica y cuánto se mejora la gestión de datos. En este trabajo, queremos reforzar con esta experiencia real, cómo el uso correcto de un MC junto con los arquetipos ayuda a diseñar, desarrollar y administrar mejores sistemas de información, enfatizando la relevancia del dominio clínico seleccionado. This work has been developed in the framework of the project PINV115-149 with the financial support of the Consejo Nacional de Ciencia y Tecnología (CONACYT)- Paraguay, with resources del FEEI and with the financial support of the Spanish State Research Agency, the Generalitat Valenciana and "Multicenter Project of Multidisciplinary Training in Cancer and Application of Electronic Health Record (EHR)", and co-financed with ERDF.

В статье при исследовании перспектив развития института удаленной работы в России предложен пакет концептуальных моделей: общей, базово-уровневой и модификацонной, который в дальнейшем позволит создать прикладную систему знаний, основная цель которой предоставление нужной информации нужным людям и в нужное время. Данное моделирование является базисом для дальнейшего моделирования, создания и развития принципиально новых подходов к организации удаленной работы. Выводы, которой позволят эффективно управлять трудовыми ресурсами предприятия.

In their daily practice, most enterprises collect, store, and manage personal information for customers in order to deliver their services. In such a setting, privacy has emerged as a key concern as companies often neglect or even misuse personal data. In response to this, governments around the world have enacted laws and regulations for privacy protection. These laws dictate privacy requirements for any system that acquires and manages personal data. Unfortunately, these requirements are often incomplete and/or inaccurate as many RE practitioners might be unsure of what exactly are privacy requirements and how are they different from other requirements, such as security. To tackle this problem, we developed a comprehensive ontology for privacy requirements. To make it comprehensive, we base our ontology on a systematic review of the literature on privacy requirements. The contributions of this work include the derivation of an ontology from a previously conducted systematic literature review, an implementation using an ontology definition tool (Protege), a demonstration of its coverage through an extensive example on Ambient Assisted Living, and a validation through a competence questionnaire answered by lexical semantics experts as well as privacy and security researchers.

In this paper, a new approach to the conceptual modeling of IoT ecosystems is presented. Taking into consideration the business processes of an organization, this approach allows the creation of models which integrate IoT-devices and IoT-governed activities into the business flow of the company. The main purpose of this research is to help organizations make faster and more efficient choices based on formalized input/output from their IoT-resources and networks. The use of modeling and formal description is necessary in order to include IoT in ERPsystems, so that IoT is considered in the overall strategic planning and decision making on all levels. ACM Computing Classification System (1998): C.2.0, K.6.0.

This paper presents development experience of architecture, principles of functioning, software engineering and modeling of multi-agent system for information support of regional business processes. The main purpose of the system is integration of distributed heterogeneous information resources and web-services devoted to innovations in the Internet. The system provides an automated search of potential business partners, an interactive innovation chains construction and selection thae most efficient of them. The necessity of system dynamics modeling methodology using in development of business processes support systems is discussed. Agent-based technologies are examined. Special attention is given to development of virtual business environment conceptual model. Our conceptual model is used as a basis for the system structure, algorithms, functionality and relationships between its main architectural components representation. Specifically, we show how to use the conceptual modeling methodology, system dynamics and multi-agent system technology in developing a multi-agent system for integration of distributed heterogeneous information resources and services devoted to innovations.

This research was aimed at eliciting the requirements of practitioners who use conceptual modeling in their professional work for the visual notations of modeling languages. While the use of conceptual modeling in practice has been addressed, what practitioners in fact require of the visual notation of the modeling languages they use has received little attention. This work was thus motivated by the need to understand to what extent practitioners’ requirements are acknowledged and accommodated by visual notation research efforts. A mixed-method study was conducted, with a survey being offered over the course of several months to LinkedIn professional groups. The requirements included in the survey were based on a leading design theory for visual notations, the Physics of Notations. After preprocessing, 104 participant responses were analyzed. Data analysis included descriptive coding and qualitative analysis of purposes for modeling and additional requirements beyond the scope of visual design. Statistical and factorial analysis was used to explore potential correlations between the importance of different requirements as perceived by practitioners and the demographic factors (e.g., domain, purpose, topics). The results indicate several correlations between demographic factors and the perceived importance of visual notation requirements, as well as differences in the perceived relative importance of different requirements for models used to communicate with modeling experts as compared to non-experts. Furthermore, the results show an evolution from trends identified in studies conducted in the previous decade. The identified correlations with practitioners’ demographics reveal several research challenges that should be addressed, as well as the potential benefits of more purpose-specific tailoring of visual notation design. Furthermore, the shift in practitioner demographics as compared to those found in earlier work indicates that the research and development of conceptual modeling efforts needs to stay up-to-date with the way practitioners employ conceptual modeling.

The article is aimed to draw the attention to the essential features of the business processes and business systems in the context of their modeling. We follow the root ideas of cybernetics in order to explain the concept of intentionality and its consequences in business system modeling as well as its impact on the business process modeling methodology and language. Possible way of refl ecting these ideas in the business processes modeling methodology is outlined using the example of the process meta-model and business process patterns from the MMABP methodology. Then the concept of causality in business systems is explained and relevant informatics theories and techniques for modeling the real world causality are introduced in this context. Particular attention is paid to the topic of relationships between both basic types of the business system models: intentional business process and causal ontology model. General rules and principles of the consistency of models are discussed together with their methodological consequences. Basic types of the consistency of models – completeness and correctness – are identifi ed and also the specifi c topic of ‘structural consistency’ is introduced in this context. In the conclusions section the needed refl ection of these essential features in the modeling languages and methodology is analyzed and the basic insuffi ciencies of the contemporary approaches to the business process modeling are pointed out together with the outline of possible ways of their overcoming. As the main challenges in the fi eld of business process modeling methodology and languages we particularly identify the need for respecting all consequences of the fact that business process has to be always taken as a purposeful process as well as the need for implementing the self-perception of the process in order to allow it achieving the higher orders of prediction.

In this work, the pervaporation-distillation hybrid process is proposed to accomplish the separation of the azeotropic mixture methanol-methyl acetate.First, the separation performance of four membranes of a medium-high flux (Pervap 2256, Pervap 2255-30, PolyAl TypM1 and PolyAn) is analyzed with the aid the pervaporation separation index (PSI). For the commercial Pervap 2256 membrane, pervaporation experiments were conducted to characterize its separation performance especially at compositions of the mixture around the azeotrope.Then, a novel screening approach, which is based on the overall cost of the separation corresponding to the optimal Hybrid process design, is proposed. For feed compositions in the range 0.1?0.9 mol/mol, several process configurations are optimized varying the compositions of the retentate and distillate streams. Quasi-optimal operation temperatures, with correspond to maximum feasible ones, are 348.15 K for the Pervap membranes and 363.15 K for the PolyAn membranes, respectively. The studied membranes are successfully and consistently classified by their respective total cost. The designs corresponding to PolyAl TypM1 and Pervap 2256 membranes present the lowest overall costs.Finally, detailed costs of the vacuum condensing system were also taken into account for the case of the Pervap 2256 membrane to include the permeate pressure as an optimization variable. The optimal value of the permeate pressure is 100 mbar and the corresponding condensation temperature is about 272 K. Fil: Figueroa Paredes, Danilo Alexander. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Laoretani, Daniela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Zelin, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigaciones en Catálisis y Petroquímica ; Argentina Fil: Vargas, Rafael Roque Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Vecchietti, Aldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Espinosa, Hector Jose Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina

System thinking has become an effective strategy when dealing with complex systems. Such systems are characterized by mutual interactions, causality and inter-dependency between system components. A typical example is the cooperation between governmental organizations and stakeholder interaction. The complexity of developing an e-government system suggests a more fundamental approach, where the roles of domain expert and system analyst are clearly separated. The main focus of this article is (1) to propose a linguistically-based systematic approach to the construction of models for the dynamics of complex systems, and (2) to propose extended causal diagrams. Our research methodology is based on Design Science. We start from a conceptual language developed for the application domain at hand and use this to define the dynamic factors. Then, we show how the resulting extended causal diagram is transformed into a framework for System Dynamics. We have demonstrated this approach by using a basic form of an e-Government as a running example. Our intention is to use this approach as a basis for a systematic step-wise introduction of e-Government in Zanzibar. Besides, this method is useful for modeling any complex system, especially for the description and evaluation of intended policies.

Within an enterprise, various stakeholders create different conceptual models, such as process, data, and requirements models. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they differ in focus, use different modelling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artefacts; as such, they typically lack consistency and interoperability. This issue can be solved by enterprise-specific ontologies, which serve as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models interoperable and facilitates model integration. The challenge to accomplish this is twofold: on the one hand, an up-to-date enterprise-specific ontology needs to be created and maintained, and on the other hand, different modellers also need to be supported in their use of the enterprise-specific ontology. In this article, we propose to tackle these challenges by means of a recommendation-based conceptual modelling and an ontology evolution framework, and we focus in particular on ontology-based modelling support. To this end, we present our framework for Business Process Modelling Notation (BPMN) as a conceptual modelling language, and focus on how modellers can be assisted during the modelling process and how this impacts the semantic quality of the resulting models. Subsequently, we present a first, large-scale explorative experiment involving 140 business students to evaluate the BPMN instantiation of our framework. The experiments show promising results with regard to incurred overhead, intention of use and model interoperability.

Trust is a key component of relationships in social life. It is commonly argued that trust is the \glue" that holds families, societies, organizations and companies together. In the literature trust is frequently considered as a strategic asset for organizations. Having a clear understanding of the notion of trust and its components is paramount to both trust assessment and trust management. Although much progress has been made to clarify the ontological nature of trust, the term remains overloaded and there is not yet a shared or prevailing, and conceptually clear notion of trust. In this paper we address this issue by means of an in-depth ontological analysis of the notion of trust, grounded in the Unified Foundational Ontology. As a result, we propose a concrete artifact, namely, the Reference Ontology for Trust, in which we characterize the general concept of trust and distinguish between two types of trust, namely, social trust and institution-based trust. We also represent the emergence of risk from trust relations. In addition, we make a comparative analysis of our Reference Ontology to other trust ontologies. To validate and demonstrate the contribution of our approach, we apply it to model two application examples.

For over a decade now, a community of researchers has contributed to the ontological foundations of Conceptual Modeling by participating to the development of the Unified Foundational Ontology (UFO) and the UFO-based modeling language OntoUML, which have been successfully employed in a num- ber of different sectors. The empirical feedback from these experiences led us to reconsider UFO's theory of relations, proposing a new theory that has already been applied to model subtle notions in the business domain, such as value, risk, service, and contract. In this paper, we advance a first formal characterization of this new theory, which is then used to design a new metamodel for OntoUML.

В статье при исследовании процесса распределения трудовых ресурсов предприятия в зависимости от сезонных спадов спроса предложен пакет концептуальных моделей: общей, базово-уровневой и модификацонной, который в дальнейшем позволит создать прикладную систему знаний, основная цель которой предоставление нужной информации нужным людям и в нужное время. Данное моделирование является базисом для дальнейшего моделирования для создания и развития сбалансированной системы распределения трудовых ресурсов в компании. В статье представлены рекомендации по распределению трудовых ресурсов на основе составления Матрицы БКГ, выводы по которой позволят эффективно управлять трудовыми ресурсами предприятия.

The progresses in ICT brought, within the virtual reconstruction of cultural heritage activity, new theoretical problems related to the interpretation of the meaning (Metadata) and of construction methods (provenance and Paradata) related to information/sources in form of digital 3D data sets. The base for a scientific approach concerns the conceptual modelling and cognitive process in 3D virtual reconstruction. The paper is focused on its theoretical definition as well as on proposing a methodological approach to data modeling behind the 3D reconstructive process. A proper conceptual modeling of the process of virtual reconstruction becomes of fundamental importance in order to take into account all the aspects that come into play: subjectivity, ontic and epistemic vagueness. The workflow and the methodology are related to the whole of the acquired and used to produce not only the 3D model, but above all the semantic characterization and enrichment of the elements that compose the model. They are based on these operational pipeline steps: 1. Subjective and temporal characterization 2. 3D virtual modeling 3. Semantic enrichment of 3D reconstructed model The methodological approach proposed, therefore, allows many types of analysis and could introduce new and meaningful innovations to the interpretation methods and techniques concerning architecture. It defines the conceptual model of data that is the basis of the reconstruction process, as well as it provides the appropriate degree of transparency of the adopted process, allowing all researchers to re-think that process in order to qualify the outcome obtained and to propose, based on new knowledge or interpretations, new reconstructive hypotheses.

Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by supporting MSPs with innovative tools for obtaining, organizing and using data to inform decision-making. The purpose of this paper is to describe and analyze the development of a knowledge-based infrastructure to support MSP decision-making processes. The paper emerged from a study to define specifications for a knowledge-based infrastructure to provide decision support for community-level MSPs in the Canadian province of Quebec. As part of the study, a process assessment was conducted to understand the needs of communities as they collect, organize, and analyze data to make decisions about their priorities. The result of this process is a “portrait”, which is an epidemiological profile of health and nutrition in their community. Portraits inform strategic planning and development of interventions, and are used to assess the impact of interventions. Our key findings indicate ambiguities and disagreement among MSP decision-makers regarding causal relationships between actions and outcomes, and the relevant data needed for making decisions. MSP decision-makers expressed a desire for easy-to-use tools that facilitate the collection, organization, synthesis, and analysis of data, to enable decision-making in a timely manner. Findings inform conceptual modeling and ontological analysis to capture the domain knowledge and specify relationships between actions and outcomes. This modeling and analysis provide the foundation for an ontology, encoded using OWL 2 Web Ontology Language. The ontology is developed to provide semantic support for the MSP process, defining objectives, strategies, actions, indicators, and data sources. In the future, software interacting with the ontology can facilitate interactive browsing by decision-makers in the MSP in the form of concepts, instances, relationships, and axioms. Our ontology also facilitates the integration and interpretation of community data, and can help in managing semantic interoperability between different knowledge sources. Future work will focus on defining specifications for the development of a database of indicators and an information system to help decision-makers to view, analyze and organize indicators for their community. This work should improve MSP decision-making in the development of interventions to address childhood obesity.

Enterprises have been challenged to adopt practices of sustainability to benefit shareholders and society with goods standing much beyond monetary profit or required by law. In combination with environmental and economic concerns, Corporate Social Responsibility (CSR) has become an option to leverage businesses with good reputation and to attract sustainability-aware market segments. In line with such a demand, this paper presents a systematic literature review of conceptual modeling studies referring explicitly to certifications, laws or norms of CSR. The more specific research goal of this work is to discover ontologies for representing CSR best practices, design patterns or policies. In total, 921 peer-reviewed papers were analyzed, from which only 17 were considered relevant for data extraction. The main result of this work is the identification of a research gap in explicit knowledge representation of CSR practices for Information Systems design, which ought to be filled to complement the (dominant) economic perspective on sustainability.

As system complexity is on the rise, there is a growing need for standardized building blocks to increase the likelihood of systems’ success. Conceptual modeling is the primary activity required for engineering systems to be understood, designed, and managed. Modern modeling languages enable describing the requirements and design of systems in a formal yet understandable way. These languages use stereotypes to standardize, clarify the model semantics, and extend the meaning of model elements. An Internet of things (IoT) system serves as an example to show the significant contributions of stereotypes to model construction, comprehension, error reduction, and increased productivity during design, simulation, and combined hardware–software system execution. This research emphasizes stereotype features that are unique to Object-Process Methodology (OPM) ISO 19450, differentiating it from stereotypes in other conceptual modeling languages. We present the implementation of stereotypes in OPCloud, an OPM modeling software environment, explore stereotype-related problems, propose solutions, and discuss future enhancements.

The business is an abstraction of the way in which value is created and delivered. The concrete representation is the business model, expressed by a group of artifacts built with different languages. It serves to describe, explain, analyze, design, and evaluate the business. The set of concepts, construction rules, artifacts, and languages required to express it, are defined by a Meta-Business Model (MBM). Multiple authors have proposed different MBMs, each one with a specific motivation and objective. Some of these MBMs are widely recognized and have been applied in contexts like innovation and entrepreneurship. Due to new challenges, such as sustainability, being faced by businesses and given new ways of producing and delivering value, like the sharing economy, Novel Complex Businesses (NCBs) are emerging. NCBs are businesses characterized by circular structures made out of numerous inter-related components, and by creating value out of the product/service schema. While existing MBMs fulfill certain purposes, they do not have the expressiveness required to describe NCBs precisely enough to describe and analyze them. This paper introduces an MBM with the concepts, construction rules, and graphical notation needed to represent NCBs. We also illustrate an NCB and present the results of the validation for our MBM.

Context: There are numerous textual notations and techniques that can be used in requirements engineering. Currently, practitioners make a choice without having scientific evidence regarding their suitability for given tasks. This uninformed choice may affect task performance. Objective: In this research, we investigate the adequacy of two well-known notations: use cases and user stories, as a starting point for the manual derivation of a structural conceptual model that represents the domain of the system. We also examine other factors that may affect the performance of this task. Methods: This work relies on two experiments. The first is a controlled classroom experiment. The second one is a quasi-experiment, conducted over multiple weeks, that aims at evaluating the quality of the derived conceptual model in light of the notation used, the adopted derivation process, and the complexity of the system to be. We measure quality in terms of validity and completeness of the conceptual model. Results: The results of the controlled experiment indicate that, for deriving conceptual models, user stories fit better than use cases. Yet, the second experiment indicates that the quality of the derived conceptual models is affected mainly by the derivation process and by the complexity of the case rather than the notation used. Contribution: We present evidence that the task of deriving a conceptual model is affected significantly by additional factors other than requirements notations. Furthermore, we propose implications and hypotheses that pave the way for further studies that compare alternative notations for the same task as well as for other tasks. Practitioners may use our findings to analyze the factors that affect the quality of the conceptual model when choosing a requirements notation and an elicitation technique that best fit their needs.

The role of concept as the trigger of a creative architectural process is investigated. Conceptual modeling is considered as a method of developing a conceptual design. The signs of the conceptual mechanism at work in architectural designing are identified, the network nature of this process is shown, and the outcome and output of conceptual modeling are described.

статья посвящена разработке концептуального моделирования экосистемы рыбоводного пруда и её проверке на качественную устойчивость. Выбор переменных модели производится в зависимости от подбора видового состава рыб, поставленных целей и задач исследования.the article is devoted to the development of a fishpond ecosystem’s conceptual model and its quality stability verification. The choice of variables in the model is made according to the selection of fish species, goals and objectives of the research.